0525.ipynb

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      "mount_file_id": "1ZK1kDW81iO9z8-CVbjaR3u0toLil43eJ",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/heejjj/Sleeping_Project/blob/main/0525.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "import pandas as pd\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "AFCSvdlrjlIX"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(\"/content/drive/MyDrive/KDT_project/HACKATHON/data_edit\")"
      ],
      "metadata": {
        "id": "ScJFxC7jkDDL"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/Sleep_Data_1 (1).csv')"
      ],
      "metadata": {
        "id": "JjzrbSFNkWoT"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_list = os.listdir('/content/drive/MyDrive/KDT_project/HACKATHON/data_edit')\n",
        "\n",
        "file_name = []\n",
        "for file in file_list:\n",
        "  if file.count('.') == 1:\n",
        "    name = file.split('.')[0]\n",
        "    file_name.append(name)\n",
        "  else:\n",
        "    for k in range(len(file) - 1, 0, -1):\n",
        "      if file[k] == '.':\n",
        "        file_name.append(file[:k])\n",
        "        break\n",
        "\n",
        "file_name"
      ],
      "metadata": {
        "id": "gAZOm7RBjw4m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "246b342c-6187-4477-ebe1-94ac4561197d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['April_16',\n",
              " 'April_11',\n",
              " 'April_14',\n",
              " 'April_15',\n",
              " 'April_12',\n",
              " 'April_13',\n",
              " 'April_17',\n",
              " 'April_18',\n",
              " 'April_19',\n",
              " 'April_1',\n",
              " 'April_10',\n",
              " 'April_3',\n",
              " 'April_5',\n",
              " 'Feb_26',\n",
              " 'April_6',\n",
              " 'April_26',\n",
              " 'April_30',\n",
              " 'April_25',\n",
              " 'April_2',\n",
              " 'April_7',\n",
              " 'April_9',\n",
              " 'April_21',\n",
              " 'April_23',\n",
              " 'April_20',\n",
              " 'April_4',\n",
              " 'April_24',\n",
              " 'April_29',\n",
              " 'April_27',\n",
              " 'April_28',\n",
              " 'March_1',\n",
              " 'Feb_27',\n",
              " 'March_18',\n",
              " 'March_13',\n",
              " 'March_22',\n",
              " 'March_12',\n",
              " 'March_16',\n",
              " 'March_10',\n",
              " 'April_8',\n",
              " 'March_15',\n",
              " 'March_19',\n",
              " 'March_14',\n",
              " 'March_17',\n",
              " 'Feb_28',\n",
              " 'March_11',\n",
              " 'March_20',\n",
              " 'March_2',\n",
              " 'March_7',\n",
              " 'March_31',\n",
              " 'March_25',\n",
              " 'March_29',\n",
              " 'March_30',\n",
              " 'March_5',\n",
              " 'March_6',\n",
              " 'March_27',\n",
              " 'March_8',\n",
              " 'March_3',\n",
              " 'March_24',\n",
              " 'March_4',\n",
              " 'March_23',\n",
              " 'March_28',\n",
              " 'May_3',\n",
              " 'May_2',\n",
              " 'March_9',\n",
              " 'May_1']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## 해당 경로에 있는 .csv 파일명 리스트 가져오기\n",
        "\n",
        "import os\n",
        "\n",
        "path = '/content/drive/MyDrive/KDT_project/HACKATHON/data_edit'\n",
        "file_list = os.listdir(path)\n",
        "file_list_py = [file for file in file_list if file.endswith('.csv')] ## 파일명 끝이 .csv인 경우"
      ],
      "metadata": {
        "id": "EEtlzzbflO4q"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_list_py"
      ],
      "metadata": {
        "id": "XQRDlIolluQ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11639f8f-acde-4449-8490-49b811e1d0bd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['April_16.csv',\n",
              " 'April_11.csv',\n",
              " 'April_14.csv',\n",
              " 'April_15.csv',\n",
              " 'April_12.csv',\n",
              " 'April_13.csv',\n",
              " 'April_17.csv',\n",
              " 'April_18.csv',\n",
              " 'April_19.csv',\n",
              " 'April_1.csv',\n",
              " 'April_10.csv',\n",
              " 'April_3.csv',\n",
              " 'April_5.csv',\n",
              " 'Feb_26.csv',\n",
              " 'April_6.csv',\n",
              " 'April_26.csv',\n",
              " 'April_30.csv',\n",
              " 'April_25.csv',\n",
              " 'April_2.csv',\n",
              " 'April_7.csv',\n",
              " 'April_9.csv',\n",
              " 'April_21.csv',\n",
              " 'April_23.csv',\n",
              " 'April_20.csv',\n",
              " 'April_4.csv',\n",
              " 'April_24.csv',\n",
              " 'April_29.csv',\n",
              " 'April_27.csv',\n",
              " 'April_28.csv',\n",
              " 'March_1.csv',\n",
              " 'Feb_27.csv',\n",
              " 'March_18.csv',\n",
              " 'March_13.csv',\n",
              " 'March_22.csv',\n",
              " 'March_12.csv',\n",
              " 'March_16.csv',\n",
              " 'March_10.csv',\n",
              " 'April_8.csv',\n",
              " 'March_15.csv',\n",
              " 'March_19.csv',\n",
              " 'March_14.csv',\n",
              " 'March_17.csv',\n",
              " 'Feb_28.csv',\n",
              " 'March_11.csv',\n",
              " 'March_20.csv',\n",
              " 'March_2.csv',\n",
              " 'March_7.csv',\n",
              " 'March_31.csv',\n",
              " 'March_25.csv',\n",
              " 'March_29.csv',\n",
              " 'March_30.csv',\n",
              " 'March_5.csv',\n",
              " 'March_6.csv',\n",
              " 'March_27.csv',\n",
              " 'March_8.csv',\n",
              " 'March_3.csv',\n",
              " 'March_24.csv',\n",
              " 'March_4.csv',\n",
              " 'March_23.csv',\n",
              " 'March_28.csv',\n",
              " 'May_3.csv',\n",
              " 'May_2.csv',\n",
              " 'March_9.csv',\n",
              " 'May_1.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 폴더에 있는 파일 가져와서 데이터프레임으로 합치기\n",
        "data_concat = pd.DataFrame()\n",
        "\n",
        "for files in file_list_py:\n",
        "    df= pd.read_csv(files)\n",
        "    data_concat = pd.concat([data_concat, df])"
      ],
      "metadata": {
        "id": "-hoTA6Rdlyt_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_concat = data_concat.drop('Unnamed: 0', axis=1)\n",
        "data_concat   # 817 rows × 7 columns"
      ],
      "metadata": {
        "id": "aYevO_0Dl2LK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "43c24a72-15c1-4456-b813-ad6c2dc862d5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Start                  End  Duration (hr)  Value  \\\n",
              "0   2023-04-16 00:53:34  2023-04-16 01:10:04          0.275      2   \n",
              "1   2023-04-16 01:10:04  2023-04-16 01:43:04          0.550      1   \n",
              "2   2023-04-16 01:43:04  2023-04-16 02:23:04          0.667      2   \n",
              "3   2023-04-16 02:23:04  2023-04-16 02:31:34          0.142      1   \n",
              "4   2023-04-16 02:31:34  2023-04-16 03:15:04          0.725      2   \n",
              "..                  ...                  ...            ...    ...   \n",
              "33  2023-05-01 10:33:56  2023-05-01 10:46:56          0.217      3   \n",
              "34  2023-05-01 10:46:56  2023-05-01 10:47:26          0.008      4   \n",
              "35  2023-05-01 10:47:26  2023-05-01 10:50:56          0.058      2   \n",
              "36  2023-05-01 10:50:56  2023-05-01 10:57:26          0.108      3   \n",
              "37  2023-05-01 10:57:26  2023-05-01 11:29:26          0.533      2   \n",
              "\n",
              "                  Source year_month_day  weekday  \n",
              "0   Sujeong의 Apple Watch     2023-04-16        6  \n",
              "1   Sujeong의 Apple Watch     2023-04-16        6  \n",
              "2   Sujeong의 Apple Watch     2023-04-16        6  \n",
              "3   Sujeong의 Apple Watch     2023-04-16        6  \n",
              "4   Sujeong의 Apple Watch     2023-04-16        6  \n",
              "..                   ...            ...      ...  \n",
              "33  Sujeong의 Apple Watch     2023-05-01        0  \n",
              "34  Sujeong의 Apple Watch     2023-05-01        0  \n",
              "35  Sujeong의 Apple Watch     2023-05-01        0  \n",
              "36  Sujeong의 Apple Watch     2023-05-01        0  \n",
              "37  Sujeong의 Apple Watch     2023-05-01        0  \n",
              "\n",
              "[1893 rows x 7 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start</th>\n",
              "      <th>End</th>\n",
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              "      <th>0</th>\n",
              "      <td>2023-04-16 00:53:34</td>\n",
              "      <td>2023-04-16 01:10:04</td>\n",
              "      <td>0.275</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-04-16 01:10:04</td>\n",
              "      <td>2023-04-16 01:43:04</td>\n",
              "      <td>0.550</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-04-16 01:43:04</td>\n",
              "      <td>2023-04-16 02:23:04</td>\n",
              "      <td>0.667</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-04-16 02:23:04</td>\n",
              "      <td>2023-04-16 02:31:34</td>\n",
              "      <td>0.142</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-04-16 02:31:34</td>\n",
              "      <td>2023-04-16 03:15:04</td>\n",
              "      <td>0.725</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023-05-01 10:33:56</td>\n",
              "      <td>2023-05-01 10:46:56</td>\n",
              "      <td>0.217</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023-05-01 10:46:56</td>\n",
              "      <td>2023-05-01 10:47:26</td>\n",
              "      <td>0.008</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2023-05-01 10:47:26</td>\n",
              "      <td>2023-05-01 10:50:56</td>\n",
              "      <td>0.058</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-05-01 10:50:56</td>\n",
              "      <td>2023-05-01 10:57:26</td>\n",
              "      <td>0.108</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2023-05-01 10:57:26</td>\n",
              "      <td>2023-05-01 11:29:26</td>\n",
              "      <td>0.533</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1893 rows × 7 columns</p>\n",
              "</div>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1fb58ccf-d2a1-4eb8-b26c-343e3388a983 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Mon = data_concat[data_concat[\"weekday\"]==0]\n",
        "Tue = data_concat[data_concat[\"weekday\"]==1]\n",
        "Wed = data_concat[data_concat[\"weekday\"]==2]\n",
        "Thur = data_concat[data_concat[\"weekday\"]==3]\n",
        "Fri = data_concat[data_concat[\"weekday\"]==4]\n",
        "Sat = data_concat[data_concat[\"weekday\"]==5]\n",
        "Sun = data_concat[data_concat[\"weekday\"]==6]"
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      "metadata": {
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      "execution_count": 9,
      "outputs": []
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      "execution_count": 10,
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              "Start              2023-05-01 10:57:26\n",
              "End                2023-05-01 11:29:26\n",
              "Duration (hr)                     1.58\n",
              "Value                                4\n",
              "Source            Sujeong의 Apple Watch\n",
              "year_month_day              2023-05-01\n",
              "weekday                              0\n",
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              "14  Sujeong의 Apple Watch     2023-05-02        1  \n",
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              "                  Start                  End  Duration (hr)  Value  \\\n",
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              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2023-05-01 10:57:26</td>\n",
              "      <td>2023-05-01 11:29:26</td>\n",
              "      <td>0.533</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>254 rows × 7 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      "execution_count": 13,
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              "                    Start                  End  Duration (hr)  Value  \\\n",
              "0     2023-02-13 23:21:07  2023-02-14 08:47:48          9.440  InBed   \n",
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              "...                   ...                  ...            ...    ...   \n",
              "2950  2023-05-03 06:44:05  2023-05-03 07:09:05          0.417    REM   \n",
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              "4     Sujeong의 Apple Watch  \n",
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              "2951  Sujeong의 Apple Watch  \n",
              "2952  Sujeong의 Apple Watch  \n",
              "2953  Sujeong의 Apple Watch  \n",
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              "      <th>2953</th>\n",
              "      <td>2023-05-03 07:30:05</td>\n",
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              "      <th>2954</th>\n",
              "      <td>2023-05-03 07:30:05</td>\n",
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              "      <td>0.017</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "</table>\n",
              "<p>2955 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-107539af-3634-4227-aaf4-4ae2ae199394')\"\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-107539af-3634-4227-aaf4-4ae2ae199394');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          "metadata": {},
          "execution_count": 13
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    {
      "cell_type": "code",
      "source": [
        "data['Start'] = pd.to_datetime(data['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')"
      ],
      "metadata": {
        "id": "T-PTaXO8sMS0"
      },
      "execution_count": 14,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "start_data = data['Start']\n"
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      "metadata": {
        "id": "Avdn0-ofqDs4"
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      "execution_count": 15,
      "outputs": []
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    {
      "cell_type": "code",
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        "start_data.mean()"
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      "metadata": {
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        "id": "kT_-SDIUqUsK",
        "outputId": "d40824d7-4220-4fb1-fd34-15dcad071624"
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      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2023-03-23 16:00:32.219289344')"
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          "execution_count": 16
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    {
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        "InBed = data[data[\"Value\"]=='InBed']\n",
        "NHC = InBed[InBed['Source']=='NHC']"
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      "metadata": {
        "id": "zkAFD0insDAz"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "InBed"
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      "metadata": {
        "colab": {
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        "id": "Pi6os6RWt_Qi",
        "outputId": "8aa05783-484c-440f-e1e6-cf61d61f41e9"
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      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Duration (hr)  Value  \\\n",
              "0    2023-02-13 23:21:07  2023-02-14 08:47:48          9.440  InBed   \n",
              "36   2023-02-14 23:16:52  2023-02-15 09:00:24          9.730  InBed   \n",
              "38   2023-02-14 23:27:04  2023-02-14 23:29:04          0.033  InBed   \n",
              "41   2023-02-14 23:31:04  2023-02-15 00:40:34          1.160  InBed   \n",
              "46   2023-02-15 00:41:04  2023-02-15 00:58:34          0.292  InBed   \n",
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              "2943 2023-05-03 04:44:35  2023-05-03 06:15:05          1.510  InBed   \n",
              "2949 2023-05-03 06:16:05  2023-05-03 07:29:05          1.220  InBed   \n",
              "2954 2023-05-03 07:30:05  2023-05-03 07:31:05          0.017  InBed   \n",
              "\n",
              "                    Source  \n",
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              "38    Sujeong의 Apple Watch  \n",
              "41    Sujeong의 Apple Watch  \n",
              "46    Sujeong의 Apple Watch  \n",
              "...                    ...  \n",
              "2930  Sujeong의 Apple Watch  \n",
              "2933  Sujeong의 Apple Watch  \n",
              "2943  Sujeong의 Apple Watch  \n",
              "2949  Sujeong의 Apple Watch  \n",
              "2954  Sujeong의 Apple Watch  \n",
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              "[688 rows x 5 columns]"
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              "      <td>NHC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2023-02-14 23:27:04</td>\n",
              "      <td>2023-02-14 23:29:04</td>\n",
              "      <td>0.033</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2023-02-14 23:31:04</td>\n",
              "      <td>2023-02-15 00:40:34</td>\n",
              "      <td>1.160</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2023-02-15 00:41:04</td>\n",
              "      <td>2023-02-15 00:58:34</td>\n",
              "      <td>0.292</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2930</th>\n",
              "      <td>2023-05-02 23:58:05</td>\n",
              "      <td>2023-05-02 23:59:05</td>\n",
              "      <td>0.017</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2933</th>\n",
              "      <td>2023-05-03 00:01:35</td>\n",
              "      <td>2023-05-03 04:43:35</td>\n",
              "      <td>4.700</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2943</th>\n",
              "      <td>2023-05-03 04:44:35</td>\n",
              "      <td>2023-05-03 06:15:05</td>\n",
              "      <td>1.510</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2949</th>\n",
              "      <td>2023-05-03 06:16:05</td>\n",
              "      <td>2023-05-03 07:29:05</td>\n",
              "      <td>1.220</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2954</th>\n",
              "      <td>2023-05-03 07:30:05</td>\n",
              "      <td>2023-05-03 07:31:05</td>\n",
              "      <td>0.017</td>\n",
              "      <td>InBed</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>688 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42082854-d71f-490f-9728-bbeef731b1d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-42082854-d71f-490f-9728-bbeef731b1d9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "InBed = InBed['Start']"
      ],
      "metadata": {
        "id": "KoQCVonfuAZb"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "InBed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0JKD9FhuG_D",
        "outputId": "85877c45-e5d1-4f8f-b28e-dca66689fafd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2023-02-13 23:21:07\n",
              "36     2023-02-14 23:16:52\n",
              "38     2023-02-14 23:27:04\n",
              "41     2023-02-14 23:31:04\n",
              "46     2023-02-15 00:41:04\n",
              "               ...        \n",
              "2930   2023-05-02 23:58:05\n",
              "2933   2023-05-03 00:01:35\n",
              "2943   2023-05-03 04:44:35\n",
              "2949   2023-05-03 06:16:05\n",
              "2954   2023-05-03 07:30:05\n",
              "Name: Start, Length: 688, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "InBed.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_RgbJXVuKHA",
        "outputId": "dc4a5b2f-ad35-42ab-d448-50b6c6c685f8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2023-03-22 19:47:37.165697536')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC = NHC['Start']"
      ],
      "metadata": {
        "id": "5QBeEf4ouMPV"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUjS7nJYu12h",
        "outputId": "a5eabcb7-f030-4ef4-9b98-3774d80a70c3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2023-03-23 18:07:36.279569920')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YboeWhGu_Ot",
        "outputId": "fe1cf734-f1cf-409a-cb7c-338b385500f4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2023-05-02 23:52:35')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.min()"
      ],
      "metadata": {
        "id": "12oY06hDvEYx",
        "outputId": "fed404f6-23fe-4b1c-9b94-920bee6c717d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2023-02-13 23:21:07')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#= pd.to_datetime(sleep_start2['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "#slst = sleep_start\n",
        "\n",
        "# object 타입을 datetime64[ns] 타입으로 바꾼다 \n",
        "NHC['Sl_Start'] = pd.to_datetime(NHC['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "NHC['slst_month']   = NHC['Sl_Start'].dt.month\n",
        "NHC['slst_month_day']   = NHC['Sl_Start'].dt.month\n",
        "NHC['slst_day']   = NHC['Sl_Start'].dt.day          # 일(숫자)\n",
        "NHC['slst_time']  = NHC['Sl_Start'].dt.time         # HH:MM:SS(문자)\n",
        "NHC['slst_hour']  = NHC['Sl_Start'].dt.hour         # 시(숫자)\n",
        "NHC['slst_minute'] = NHC['Sl_Start'].dt.minute       # 분(숫자)\n",
        "NHC['slst_second'] = NHC['Sl_Start'].dt.second       # 초(숫자)\n",
        "\n",
        "NHC.head(50)"
      ],
      "metadata": {
        "id": "YFdyDPswvFZ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "f5638559-af3d-4664-cb2e-3584ddd40552"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Start'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-ec7fcfd08508>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# object 타입을 datetime64[ns] 타입으로 바꾼다\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mNHC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sl_Start'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNHC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Start'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mNHC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'slst_month'\u001b[0m\u001b[0;34m]\u001b[0m   \u001b[0;34m=\u001b[0m \u001b[0mNHC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sl_Start'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1088\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1089\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1090\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Start'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.head(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-spmOqo82tS",
        "outputId": "259cb12d-b9be-4ca3-a580-1e6713751261"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2023-02-13 23:21:07\n",
              "36     2023-02-14 23:16:52\n",
              "93     2023-02-15 23:50:07\n",
              "138    2023-02-16 06:36:50\n",
              "160    2023-02-17 00:40:33\n",
              "161    2023-02-18 00:33:14\n",
              "214    2023-02-18 23:57:04\n",
              "247    2023-02-19 23:50:35\n",
              "296    2023-02-21 00:18:21\n",
              "330    2023-02-21 23:53:36\n",
              "379    2023-02-23 00:07:13\n",
              "416    2023-02-23 22:38:12\n",
              "493    2023-02-25 01:40:05\n",
              "494    2023-02-25 23:34:41\n",
              "540    2023-02-26 23:25:31\n",
              "574    2023-02-27 23:37:21\n",
              "606    2023-03-01 01:03:51\n",
              "619    2023-03-01 03:14:33\n",
              "650    2023-03-01 23:58:48\n",
              "692    2023-03-03 00:25:28\n",
              "721    2023-03-04 00:01:28\n",
              "760    2023-03-05 00:03:33\n",
              "796    2023-03-05 23:31:12\n",
              "826    2023-03-06 22:45:35\n",
              "877    2023-03-07 22:49:19\n",
              "912    2023-03-08 22:50:41\n",
              "938    2023-03-09 23:51:45\n",
              "997    2023-03-10 22:53:41\n",
              "1028   2023-03-11 03:41:04\n",
              "1029   2023-03-11 03:41:41\n",
              "1074   2023-03-12 00:29:27\n",
              "1126   2023-03-13 01:46:16\n",
              "1166   2023-03-14 00:19:33\n",
              "1197   2023-03-15 00:04:29\n",
              "1223   2023-03-15 23:31:45\n",
              "1283   2023-03-16 23:41:31\n",
              "1321   2023-03-18 00:29:57\n",
              "1367   2023-03-19 00:12:48\n",
              "1392   2023-03-20 00:30:15\n",
              "1410   2023-03-21 01:16:00\n",
              "1411   2023-03-21 04:55:06\n",
              "1412   2023-03-21 05:02:32\n",
              "1413   2023-03-21 05:04:03\n",
              "1414   2023-03-21 05:06:20\n",
              "1415   2023-03-21 05:39:26\n",
              "1416   2023-03-21 05:40:09\n",
              "1417   2023-03-21 05:45:57\n",
              "1418   2023-03-21 05:59:20\n",
              "1419   2023-03-21 06:02:26\n",
              "1420   2023-03-21 06:07:03\n",
              "Name: Start, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTeRgZ-K0M2C",
        "outputId": "bbbe61f0-cc36-48a2-ea74-f1412290c546"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "Int64Index: 93 entries, 0 to 2928\n",
            "Series name: Start\n",
            "Non-Null Count  Dtype         \n",
            "--------------  -----         \n",
            "93 non-null     datetime64[ns]\n",
            "dtypes: datetime64[ns](1)\n",
            "memory usage: 1.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_start = NHC['slst_time']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "5mI29TxHxn6y",
        "outputId": "ab83918d-0900-4262-8725-28221b08415c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'slst_time'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-8f4f98100681>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mNHC_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNHC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'slst_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1088\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1089\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1090\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'slst_time'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_start"
      ],
      "metadata": {
        "id": "X_1ZUwVFzbB-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_hour = NHC['slst_hour']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "Cjk1SD9bzcyu",
        "outputId": "9729096d-d911-460c-a09e-62d452edf00a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'slst_hour'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-40cf116a45a2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mNHC_hour\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNHC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'slst_hour'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1088\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1089\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1090\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'slst_hour'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_minute = NHC['slst_minute']"
      ],
      "metadata": {
        "id": "W_QJfQPW0Hae"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_second = NHC['slst_second']"
      ],
      "metadata": {
        "id": "wVqKjeKD0oBX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N_hour=NHC_hour.sum()\n",
        "N_hour_c=NHC_hour.value_counts().sum()\n",
        "\n",
        "N_hour_evg=N_hour/N_hour_c\n",
        "N_hour_evg\n",
        "#시간평균"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "3vuXfYL-0vv7",
        "outputId": "25a9fc32-5f06-435e-d4fa-72aac1425961"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-110a4e8897c2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mN_hour\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNHC_hour\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mN_hour_c\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNHC_hour\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mN_hour_evg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_hour\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mN_hour_c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mN_hour_evg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'NHC_hour' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "N_min=NHC_minute.sum()\n",
        "N_min_c=NHC_minute.value_counts().sum()\n",
        "\n",
        "N_min_evg=N_min/N_min_c\n",
        "N_min_evg\n",
        "#분평균"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "tbwCZ3yi0_1O",
        "outputId": "ff923102-fa15-455c-9932-bf25c7b44aee"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-38b2c1ee43cc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mN_min\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNHC_minute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mN_min_c\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNHC_minute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mN_min_evg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mN_min\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mN_min_c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mN_min_evg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'NHC_minute' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "N_sec=NHC_second.sum()\n",
        "N_sec_c=NHC_second.value_counts().sum()\n",
        "\n",
        "N_sec_evg=N_sec/N_sec_c\n",
        "N_sec_evg"
      ],
      "metadata": {
        "id": "KAghygW11DHO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N_hour=(NHC_hour.sum())*60\n",
        "N_min=NHC_minute.sum()\n",
        "\n",
        "evg=(N_hour+N_min)/60\n",
        "evg\n",
        "#시간-> 분으로 변환"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "U7XRNr_02Vwq",
        "outputId": "7e3cdf46-9234-40d8-bc0d-77c298756a5b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-b9912707792d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mN_hour\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNHC_hour\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mN_min\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNHC_minute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mevg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_hour\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mN_min\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mevg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'NHC_hour' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.info()"
      ],
      "metadata": {
        "id": "LpybM2Lq3OhS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "id": "lPv3sPVjdoRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NHC.NHC_dt = pd.to_datetime(NHC.Start)\n",
        "df = NHC.set_index('Start')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "DBbSxArjeI-b",
        "outputId": "ba033677-52f5-45a3-9625-675d1403934e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-8fb8fb36e461>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mNHC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNHC_dt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNHC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNHC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Start'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m         ):\n\u001b[1;32m   5901\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'Start'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cr9V0_kfgvt",
        "outputId": "433d809d-4641-4eab-dd6d-61354d7a8230"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2023-02-13 23:21:07\n",
              "36     2023-02-14 23:16:52\n",
              "93     2023-02-15 23:50:07\n",
              "138    2023-02-16 06:36:50\n",
              "160    2023-02-17 00:40:33\n",
              "               ...        \n",
              "2795   2023-04-29 01:02:56\n",
              "2829   2023-04-30 00:43:10\n",
              "2866   2023-05-01 00:42:23\n",
              "2910   2023-05-02 01:27:47\n",
              "2928   2023-05-02 23:52:35\n",
              "Name: Start, Length: 93, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newDf = df.resample(rule='T').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "zYC4VZRfhBY6",
        "outputId": "feebc92a-f018-4e40-db27-034a5d73ea5f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-c3cec8562755>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrule\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'T'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset, group_keys)\u001b[0m\n\u001b[1;32m  11390\u001b[0m         \u001b[0mgroup_keys\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoDefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mno_default\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11391\u001b[0m     ) -> Resampler:\n\u001b[0;32m> 11392\u001b[0;31m         return super().resample(\n\u001b[0m\u001b[1;32m  11393\u001b[0m             \u001b[0mrule\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrule\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11394\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset, group_keys)\u001b[0m\n\u001b[1;32m   8856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8857\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8858\u001b[0;31m         return get_resampler(\n\u001b[0m\u001b[1;32m   8859\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8860\u001b[0m             \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrule\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36mget_resampler\u001b[0;34m(obj, kind, **kwds)\u001b[0m\n\u001b[1;32m   1542\u001b[0m     \"\"\"\n\u001b[1;32m   1543\u001b[0m     \u001b[0mtg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTimeGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1544\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[0;34m(self, obj, kind)\u001b[0m\n\u001b[1;32m   1723\u001b[0m             )\n\u001b[1;32m   1724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1725\u001b[0;31m         raise TypeError(\n\u001b[0m\u001b[1;32m   1726\u001b[0m             \u001b[0;34m\"Only valid with DatetimeIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1727\u001b[0m             \u001b[0;34m\"TimedeltaIndex or PeriodIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newDf"
      ],
      "metadata": {
        "id": "-NBiWygmhHvQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1=data['Start'] = pd.to_datetime(data['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')"
      ],
      "metadata": {
        "id": "0PmHZxDJjWu5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1=data"
      ],
      "metadata": {
        "id": "acxT9yxKjsRE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data 전처리 기준 [ 규칙적인 생활(교육 시작일인 2.20일저녁부터~)데이터 사용 이전 날짜 drop \n"
      ],
      "metadata": {
        "id": "GAkCWUOwtuZl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_df = NHC[(NHC['Start'].dt.hour < 3) | (NHC['Start'].dt.hour > 12)] # InBed인것들중 Strat의 시간이 오전 3시 오후 12시 이후의 것들만 남김\n",
        "filtered_df2= NHC[(NHC['Start'].dt.hour > 3) & (NHC['Start'].dt.hour < 12)] # InBed인것들중 Strat의 시간이 오전 3시 오후 12시 이전의 것들만 남김"
      ],
      "metadata": {
        "id": "QRljCn2xj2ZW"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
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              "                   Start                  End  Duration (hr)  Value Source\n",
              "138  2023-02-16 06:36:50  2023-02-16 09:00:17          2.390  InBed    NHC\n",
              "1411 2023-03-21 04:55:06  2023-03-21 04:59:32          0.074  InBed    NHC\n",
              "1412 2023-03-21 05:02:32  2023-03-21 05:02:38          0.002  InBed    NHC\n",
              "1413 2023-03-21 05:04:03  2023-03-21 05:04:45          0.012  InBed    NHC\n",
              "1414 2023-03-21 05:06:20  2023-03-21 05:06:55          0.010  InBed    NHC\n",
              "1415 2023-03-21 05:39:26  2023-03-21 05:39:53          0.008  InBed    NHC\n",
              "1416 2023-03-21 05:40:09  2023-03-21 05:40:57          0.013  InBed    NHC\n",
              "1417 2023-03-21 05:45:57  2023-03-21 05:46:07          0.003  InBed    NHC\n",
              "1418 2023-03-21 05:59:20  2023-03-21 06:02:06          0.046  InBed    NHC\n",
              "1419 2023-03-21 06:02:26  2023-03-21 06:07:00          0.076  InBed    NHC\n",
              "1420 2023-03-21 06:07:03  2023-03-21 06:07:04          0.000  InBed    NHC\n",
              "1866 2023-04-02 09:26:37  2023-04-02 09:57:17          0.511  InBed    NHC"
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              "      <td>0.002</td>\n",
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              "      <td>2023-03-21 05:04:03</td>\n",
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              "      <td>2023-03-21 05:39:26</td>\n",
              "      <td>2023-03-21 05:39:53</td>\n",
              "      <td>0.008</td>\n",
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              "      <td>2023-03-21 05:46:07</td>\n",
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              "      <td>2023-03-21 06:02:06</td>\n",
              "      <td>0.046</td>\n",
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              "      <td>NHC</td>\n",
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              "      <td>2023-03-21 06:02:26</td>\n",
              "      <td>2023-03-21 06:07:00</td>\n",
              "      <td>0.076</td>\n",
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              "      <td>2023-04-02 09:26:37</td>\n",
              "      <td>2023-04-02 09:57:17</td>\n",
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      "execution_count": 44,
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              "                   Start                  End  Duration (hr)  Value Source\n",
              "0    2023-02-13 23:21:07  2023-02-14 08:47:48           9.44  InBed    NHC\n",
              "36   2023-02-14 23:16:52  2023-02-15 09:00:24           9.73  InBed    NHC\n",
              "93   2023-02-15 23:50:07  2023-02-16 06:36:44           6.78  InBed    NHC\n",
              "160  2023-02-17 00:40:33  2023-02-17 09:00:08           8.33  InBed    NHC\n",
              "161  2023-02-18 00:33:14  2023-02-18 09:00:42           8.46  InBed    NHC\n",
              "...                  ...                  ...            ...    ...    ...\n",
              "2795 2023-04-29 01:02:56  2023-04-29 07:30:00           6.45  InBed    NHC\n",
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              "2866 2023-05-01 00:42:23  2023-05-01 07:30:00           6.79  InBed    NHC\n",
              "2910 2023-05-02 01:27:47  2023-05-02 07:32:16           6.07  InBed    NHC\n",
              "2928 2023-05-02 23:52:35  2023-05-03 07:35:09           7.71  InBed    NHC\n",
              "\n",
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              "      <td>2023-02-17 00:40:33</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f = filtered_df[filtered_df['Duration (hr)'] > 4] # duration 의 길이가 4시간 이상인것들만 남김"
      ],
      "metadata": {
        "id": "oKvigaOhk2La"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f2 = filtered_df[filtered_df['Duration (hr)'] <= 7]# duration 의 길이가 4시간 이하인것들만 남김"
      ],
      "metadata": {
        "id": "CV8qnCI_pGUd"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f = NHC_f[[\"Start\",\"End\", \"Value\", \"Duration (hr)\"]]\n",
        "NHC_f # duration 의 길이가 4시간 이상인것들"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "WifzumC_lChn",
        "outputId": "9825c4f8-1ded-49e5-ed8c-9f1f172994e3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Value  Duration (hr)\n",
              "0    2023-02-13 23:21:07  2023-02-14 08:47:48  InBed           9.44\n",
              "36   2023-02-14 23:16:52  2023-02-15 09:00:24  InBed           9.73\n",
              "93   2023-02-15 23:50:07  2023-02-16 06:36:44  InBed           6.78\n",
              "160  2023-02-17 00:40:33  2023-02-17 09:00:08  InBed           8.33\n",
              "161  2023-02-18 00:33:14  2023-02-18 09:00:42  InBed           8.46\n",
              "...                  ...                  ...    ...            ...\n",
              "2795 2023-04-29 01:02:56  2023-04-29 07:30:00  InBed           6.45\n",
              "2829 2023-04-30 00:43:10  2023-04-30 07:31:30  InBed           6.81\n",
              "2866 2023-05-01 00:42:23  2023-05-01 07:30:00  InBed           6.79\n",
              "2910 2023-05-02 01:27:47  2023-05-02 07:32:16  InBed           6.07\n",
              "2928 2023-05-02 23:52:35  2023-05-03 07:35:09  InBed           7.71\n",
              "\n",
              "[76 rows x 4 columns]"
            ],
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              "\n",
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              "      <th>0</th>\n",
              "      <td>2023-02-13 23:21:07</td>\n",
              "      <td>2023-02-14 08:47:48</td>\n",
              "      <td>InBed</td>\n",
              "      <td>9.44</td>\n",
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              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-02-14 23:16:52</td>\n",
              "      <td>2023-02-15 09:00:24</td>\n",
              "      <td>InBed</td>\n",
              "      <td>9.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2023-02-15 23:50:07</td>\n",
              "      <td>2023-02-16 06:36:44</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.78</td>\n",
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              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>2023-02-17 00:40:33</td>\n",
              "      <td>2023-02-17 09:00:08</td>\n",
              "      <td>InBed</td>\n",
              "      <td>8.33</td>\n",
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              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>2023-02-18 00:33:14</td>\n",
              "      <td>2023-02-18 09:00:42</td>\n",
              "      <td>InBed</td>\n",
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              "      <th>2795</th>\n",
              "      <td>2023-04-29 01:02:56</td>\n",
              "      <td>2023-04-29 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2829</th>\n",
              "      <td>2023-04-30 00:43:10</td>\n",
              "      <td>2023-04-30 07:31:30</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.81</td>\n",
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              "    <tr>\n",
              "      <th>2866</th>\n",
              "      <td>2023-05-01 00:42:23</td>\n",
              "      <td>2023-05-01 07:30:00</td>\n",
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              "      <td>2023-05-02 01:27:47</td>\n",
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              "      <th>2928</th>\n",
              "      <td>2023-05-02 23:52:35</td>\n",
              "      <td>2023-05-03 07:35:09</td>\n",
              "      <td>InBed</td>\n",
              "      <td>7.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>76 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fac0a2e-1165-4a2d-b533-e3e28d17be12')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2fac0a2e-1165-4a2d-b533-e3e28d17be12 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fac0a2e-1165-4a2d-b533-e3e28d17be12');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f2 = NHC_f2[[\"Start\",\"End\", \"Value\", \"Duration (hr)\"]]\n",
        "NHC_f2 # duration 의 길이가 4시간 이하인것들 - 3.1일, 3월 21일 data 이상함 발견!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "UF-0cdxvo_XQ",
        "outputId": "0db5f852-82d3-4e95-ae84-32e82334e74c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Value  Duration (hr)\n",
              "93   2023-02-15 23:50:07  2023-02-16 06:36:44  InBed           6.78\n",
              "296  2023-02-21 00:18:21  2023-02-21 06:00:23  InBed           5.70\n",
              "493  2023-02-25 01:40:05  2023-02-25 07:30:00  InBed           5.83\n",
              "606  2023-03-01 01:03:51  2023-03-01 03:14:18  InBed           2.17\n",
              "997  2023-03-10 22:53:41  2023-03-11 03:40:39  InBed           4.78\n",
              "1126 2023-03-13 01:46:16  2023-03-13 07:30:41  InBed           5.74\n",
              "1321 2023-03-18 00:29:57  2023-03-18 07:30:00  InBed           7.00\n",
              "1392 2023-03-20 00:30:15  2023-03-20 07:30:18  InBed           7.00\n",
              "1410 2023-03-21 01:16:00  2023-03-21 04:53:26  InBed           3.62\n",
              "2080 2023-04-09 00:49:01  2023-04-09 07:30:15  InBed           6.69\n",
              "2119 2023-04-10 01:04:50  2023-04-10 07:30:14  InBed           6.42\n",
              "2340 2023-04-16 00:31:04  2023-04-16 07:30:00  InBed           6.98\n",
              "2533 2023-04-21 00:32:24  2023-04-21 07:30:14  InBed           6.96\n",
              "2582 2023-04-22 00:40:57  2023-04-22 07:30:10  InBed           6.82\n",
              "2583 2023-04-23 02:11:38  2023-04-23 07:30:00  InBed           5.31\n",
              "2772 2023-04-28 01:11:15  2023-04-28 07:30:19  InBed           6.32\n",
              "2795 2023-04-29 01:02:56  2023-04-29 07:30:00  InBed           6.45\n",
              "2829 2023-04-30 00:43:10  2023-04-30 07:31:30  InBed           6.81\n",
              "2866 2023-05-01 00:42:23  2023-05-01 07:30:00  InBed           6.79\n",
              "2910 2023-05-02 01:27:47  2023-05-02 07:32:16  InBed           6.07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c281bab6-b90a-4ad6-b31e-88c631fe08ff\">\n",
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>93</th>\n",
              "      <td>2023-02-15 23:50:07</td>\n",
              "      <td>2023-02-16 06:36:44</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>2023-02-21 00:18:21</td>\n",
              "      <td>2023-02-21 06:00:23</td>\n",
              "      <td>InBed</td>\n",
              "      <td>5.70</td>\n",
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              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>2023-02-25 01:40:05</td>\n",
              "      <td>2023-02-25 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>5.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>606</th>\n",
              "      <td>2023-03-01 01:03:51</td>\n",
              "      <td>2023-03-01 03:14:18</td>\n",
              "      <td>InBed</td>\n",
              "      <td>2.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>2023-03-10 22:53:41</td>\n",
              "      <td>2023-03-11 03:40:39</td>\n",
              "      <td>InBed</td>\n",
              "      <td>4.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1126</th>\n",
              "      <td>2023-03-13 01:46:16</td>\n",
              "      <td>2023-03-13 07:30:41</td>\n",
              "      <td>InBed</td>\n",
              "      <td>5.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1321</th>\n",
              "      <td>2023-03-18 00:29:57</td>\n",
              "      <td>2023-03-18 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1392</th>\n",
              "      <td>2023-03-20 00:30:15</td>\n",
              "      <td>2023-03-20 07:30:18</td>\n",
              "      <td>InBed</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1410</th>\n",
              "      <td>2023-03-21 01:16:00</td>\n",
              "      <td>2023-03-21 04:53:26</td>\n",
              "      <td>InBed</td>\n",
              "      <td>3.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2080</th>\n",
              "      <td>2023-04-09 00:49:01</td>\n",
              "      <td>2023-04-09 07:30:15</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2119</th>\n",
              "      <td>2023-04-10 01:04:50</td>\n",
              "      <td>2023-04-10 07:30:14</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2340</th>\n",
              "      <td>2023-04-16 00:31:04</td>\n",
              "      <td>2023-04-16 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2533</th>\n",
              "      <td>2023-04-21 00:32:24</td>\n",
              "      <td>2023-04-21 07:30:14</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2582</th>\n",
              "      <td>2023-04-22 00:40:57</td>\n",
              "      <td>2023-04-22 07:30:10</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2583</th>\n",
              "      <td>2023-04-23 02:11:38</td>\n",
              "      <td>2023-04-23 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>5.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2772</th>\n",
              "      <td>2023-04-28 01:11:15</td>\n",
              "      <td>2023-04-28 07:30:19</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2795</th>\n",
              "      <td>2023-04-29 01:02:56</td>\n",
              "      <td>2023-04-29 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2829</th>\n",
              "      <td>2023-04-30 00:43:10</td>\n",
              "      <td>2023-04-30 07:31:30</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2866</th>\n",
              "      <td>2023-05-01 00:42:23</td>\n",
              "      <td>2023-05-01 07:30:00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2910</th>\n",
              "      <td>2023-05-02 01:27:47</td>\n",
              "      <td>2023-05-02 07:32:16</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c281bab6-b90a-4ad6-b31e-88c631fe08ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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        "NHC_f2"
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      "execution_count": 49,
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              "                   Start                  End  Value  Duration (hr)\n",
              "93   2023-02-15 23:50:07  2023-02-16 06:36:44  InBed           6.78\n",
              "296  2023-02-21 00:18:21  2023-02-21 06:00:23  InBed           5.70\n",
              "493  2023-02-25 01:40:05  2023-02-25 07:30:00  InBed           5.83\n",
              "606  2023-03-01 01:03:51  2023-03-01 03:14:18  InBed           2.17\n",
              "997  2023-03-10 22:53:41  2023-03-11 03:40:39  InBed           4.78\n",
              "1126 2023-03-13 01:46:16  2023-03-13 07:30:41  InBed           5.74\n",
              "1321 2023-03-18 00:29:57  2023-03-18 07:30:00  InBed           7.00\n",
              "1392 2023-03-20 00:30:15  2023-03-20 07:30:18  InBed           7.00\n",
              "1410 2023-03-21 01:16:00  2023-03-21 04:53:26  InBed           3.62\n",
              "2080 2023-04-09 00:49:01  2023-04-09 07:30:15  InBed           6.69\n",
              "2119 2023-04-10 01:04:50  2023-04-10 07:30:14  InBed           6.42\n",
              "2340 2023-04-16 00:31:04  2023-04-16 07:30:00  InBed           6.98\n",
              "2533 2023-04-21 00:32:24  2023-04-21 07:30:14  InBed           6.96\n",
              "2582 2023-04-22 00:40:57  2023-04-22 07:30:10  InBed           6.82\n",
              "2583 2023-04-23 02:11:38  2023-04-23 07:30:00  InBed           5.31\n",
              "2772 2023-04-28 01:11:15  2023-04-28 07:30:19  InBed           6.32\n",
              "2795 2023-04-29 01:02:56  2023-04-29 07:30:00  InBed           6.45\n",
              "2829 2023-04-30 00:43:10  2023-04-30 07:31:30  InBed           6.81\n",
              "2866 2023-05-01 00:42:23  2023-05-01 07:30:00  InBed           6.79\n",
              "2910 2023-05-02 01:27:47  2023-05-02 07:32:16  InBed           6.07"
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              "      <td>InBed</td>\n",
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              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>2023-02-21 00:18:21</td>\n",
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              "      <th>493</th>\n",
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              "      <td>InBed</td>\n",
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              "      <th>606</th>\n",
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              "      <td>InBed</td>\n",
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              "      <th>997</th>\n",
              "      <td>2023-03-10 22:53:41</td>\n",
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              "      <th>1126</th>\n",
              "      <td>2023-03-13 01:46:16</td>\n",
              "      <td>2023-03-13 07:30:41</td>\n",
              "      <td>InBed</td>\n",
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              "      <th>1321</th>\n",
              "      <td>2023-03-18 00:29:57</td>\n",
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              "    <tr>\n",
              "      <th>1392</th>\n",
              "      <td>2023-03-20 00:30:15</td>\n",
              "      <td>2023-03-20 07:30:18</td>\n",
              "      <td>InBed</td>\n",
              "      <td>7.00</td>\n",
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              "      <th>1410</th>\n",
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              "      <td>2023-03-21 04:53:26</td>\n",
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              "      <th>2119</th>\n",
              "      <td>2023-04-10 01:04:50</td>\n",
              "      <td>2023-04-10 07:30:14</td>\n",
              "      <td>InBed</td>\n",
              "      <td>6.42</td>\n",
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              "      <th>2340</th>\n",
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              "      <td>InBed</td>\n",
              "      <td>6.98</td>\n",
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              "      <th>2533</th>\n",
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              "      <td>2023-04-29 01:02:56</td>\n",
              "      <td>2023-04-29 07:30:00</td>\n",
              "      <td>InBed</td>\n",
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              "      <th>2829</th>\n",
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              "      <td>InBed</td>\n",
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              "      <th>2866</th>\n",
              "      <td>2023-05-01 00:42:23</td>\n",
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              "      <th>2910</th>\n",
              "      <td>2023-05-02 01:27:47</td>\n",
              "      <td>2023-05-02 07:32:16</td>\n",
              "      <td>InBed</td>\n",
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              "    .colab-df-container {\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "data": {
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              "Start            2023-02-13 23:21:07\n",
              "End              2023-02-14 08:47:48\n",
              "Value                          InBed\n",
              "Duration (hr)                   4.78\n",
              "dtype: object"
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      "source": [
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      "execution_count": 51,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[138     143.40\n",
              " 1411      4.44\n",
              " 1412      0.12\n",
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            "memory usage: 115.6+ KB\n"
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          "text": [
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            "Int64Index: 76 entries, 0 to 2928\n",
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            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          76 non-null     datetime64[ns]\n",
            " 1   End            76 non-null     object        \n",
            " 2   Value          76 non-null     object        \n",
            " 3   Duration (hr)  76 non-null     float64       \n",
            "dtypes: datetime64[ns](1), float64(1), object(2)\n",
            "memory usage: 3.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f['Duration (hr)'].median() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvUiTBhrpclF",
        "outputId": "bac270fb-3a9c-45d7-a79d-5926225a9396"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.5649999999999995"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f['Duration (hr)'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoaEYOM5rbi4",
        "outputId": "e0fce730-826f-4218-dcde-30c54550fa9b"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.640789473684211"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f['Duration (hr)'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QO1kh0zErJzw",
        "outputId": "f0dffeb6-c0da-461d-fed7-6e4cb1943729"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.73"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NHC_f['Duration (hr)'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOLNM-t_rYdg",
        "outputId": "2e35b290-cbeb-4fb8-f16d-c7284964a36f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.78"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "F2igHa1BuiTA"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir(\"/content/drive/MyDrive/KDT_project/HACKATHON\")"
      ],
      "metadata": {
        "id": "cjlkrzxWwEVh"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime\n",
        "\n",
        "start_date = datetime.datetime(2023, 2, 21, 12, 0, 0)\n",
        "end_date = datetime.datetime(2023, 5, 2, 12, 0, 0)\n",
        "\n",
        "date_range = pd.date_range(start=start_date, end=end_date, freq='D')\n",
        "\n",
        "for date in date_range:\n",
        "    start_date_str = date.strftime('%Y-%m-%d 12:00:00')\n",
        "    end_date_str = (date + datetime.timedelta(days=1)).strftime('%Y-%m-%d 12:00:00')\n",
        "\n",
        "    filtered_data = data[(data['Start'] >= start_date_str) & (data['Start'] <= end_date_str)]\n",
        "    filtered_data = filtered_data[((filtered_data['Source'] == 'NHC') | (filtered_data['Value'] == 'InBed'))] #Inbed 인것들 전부 \n",
        "\n",
        "    filtered_data[\"year-month-day\"] = date.strftime('%Y-%m-%d')\n",
        "    filtered_data[\"weekday\"] = date.weekday()\n",
        "\n",
        "    # 현재 날짜에 해당하는 필터링된 데이터 저장\n",
        "    #file_name = f\"data_{date.strftime('%Y%m%d')}.csv\"\n",
        "    file_name = f\"data_{start_date_str}.csv\"\n",
        "    filtered_data.to_csv(file_name, index=False)"
      ],
      "metadata": {
        "id": "YOCcGC85umBW"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/drive/MyDrive/KDT_project/HACKATHON/new_edit2')"
      ],
      "metadata": {
        "id": "LN-XQ5oTy88H"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime\n",
        "\n",
        "start_date = datetime.datetime(2023, 2, 21, 12, 0, 0)\n",
        "end_date = datetime.datetime(2023, 5, 2, 12, 0, 0)\n",
        "\n",
        "date_range = pd.date_range(start=start_date, end=end_date, freq='D')\n",
        "\n",
        "for date in date_range:\n",
        "    start_date_str = date.strftime('%Y-%m-%d 12:00:00')\n",
        "    end_date_str = (date + datetime.timedelta(days=1)).strftime('%Y-%m-%d 12:00:00')\n",
        "\n",
        "    filtered_data = data[(data['Start'] >= start_date_str) & (data['Start'] <= end_date_str)]\n",
        "    filtered_data = filtered_data[((filtered_data['Source'] == 'NHC') & (filtered_data['Value'] == 'InBed'))] #Inbed 이면서 NHC인것들 \n",
        "\n",
        "    filtered_data[\"year-month-day\"] = date.strftime('%Y-%m-%d')\n",
        "    filtered_data[\"weekday\"] = date.weekday()\n",
        "\n",
        "    # 현재 날짜에 해당하는 필터링된 데이터 저장\n",
        "    #file_name = f\"data_{date.strftime('%Y%m%d')}.csv\"\n",
        "    #file_name = f\"data_{start_date_str}.csv\"\n",
        "    file_name = f\"data_{date.strftime('%Y_%m_%d')}.csv\"\n",
        "    filtered_data.to_csv(file_name, index=False)"
      ],
      "metadata": {
        "id": "Vj6YayFB0VF8"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## 해당 경로에 있는 .csv 파일명 리스트 가져오기\n",
        "\n",
        "import os\n",
        "\n",
        "path = '/content/drive/MyDrive/KDT_project/HACKATHON/new_edit2'\n",
        "file_list = os.listdir(path)\n",
        "file_list_py = [file for file in file_list if file.endswith('.csv')] ## 파일명 끝이 .csv인 경우"
      ],
      "metadata": {
        "id": "WThRij8V2LEp"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/KDT_project/HACKATHON/new_edit2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrw94cNZ4M6k",
        "outputId": "f83c28c6-b5c5-4ead-f7d0-e046b19757a0"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 폴더에 있는 파일 가져와서 데이터프레임으로 합치기\n",
        "data_concat = pd.DataFrame()\n",
        "\n",
        "for files in file_list_py:\n",
        "    df= pd.read_csv(files)\n",
        "    data_concat = pd.concat([data_concat, df])"
      ],
      "metadata": {
        "id": "qJuUzS5F4U6y"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Mon = data_concat[data_concat[\"weekday\"]==0]\n",
        "Tue = data_concat[data_concat[\"weekday\"]==1]\n",
        "Wed = data_concat[data_concat[\"weekday\"]==2]\n",
        "Thur = data_concat[data_concat[\"weekday\"]==3]\n",
        "Fri = data_concat[data_concat[\"weekday\"]==4]\n",
        "Sat = data_concat[data_concat[\"weekday\"]==5]\n",
        "Sun = data_concat[data_concat[\"weekday\"]==6]"
      ],
      "metadata": {
        "id": "fSp3XlM14bny"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Mon"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "uRsBaFrL4d7c",
        "outputId": "ceaeb45b-8648-4539-baa3-ce141d6b9400"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr)  Value Source  \\\n",
              "0  2023-02-27 23:37:21  2023-02-28 07:00:35           7.39  InBed    NHC   \n",
              "0  2023-03-06 22:45:35  2023-03-07 07:30:14           8.74  InBed    NHC   \n",
              "0  2023-03-14 00:19:33  2023-03-14 07:30:35           7.18  InBed    NHC   \n",
              "0  2023-03-27 22:38:27  2023-03-28 07:30:14           8.86  InBed    NHC   \n",
              "0  2023-04-03 23:28:26  2023-04-04 07:30:21           8.03  InBed    NHC   \n",
              "0  2023-04-11 00:18:41  2023-04-11 07:30:16           7.19  InBed    NHC   \n",
              "0  2023-04-17 23:02:04  2023-04-18 07:30:34           8.48  InBed    NHC   \n",
              "0  2023-04-25 00:28:22  2023-04-25 07:32:14           7.06  InBed    NHC   \n",
              "0  2023-05-02 01:27:47  2023-05-02 07:32:16           6.07  InBed    NHC   \n",
              "\n",
              "  year-month-day  weekday  \n",
              "0     2023-02-27        0  \n",
              "0     2023-03-06        0  \n",
              "0     2023-03-13        0  \n",
              "0     2023-03-27        0  \n",
              "0     2023-04-03        0  \n",
              "0     2023-04-10        0  \n",
              "0     2023-04-17        0  \n",
              "0     2023-04-24        0  \n",
              "0     2023-05-01        0  "
            ],
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start</th>\n",
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              "      <td>7.39</td>\n",
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              "      <td>2023-03-06 22:45:35</td>\n",
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              "      <td>7.18</td>\n",
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              "      <td>2023-03-27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-03 23:28:26</td>\n",
              "      <td>2023-04-04 07:30:21</td>\n",
              "      <td>8.03</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-03</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-11 00:18:41</td>\n",
              "      <td>2023-04-11 07:30:16</td>\n",
              "      <td>7.19</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-10</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-17 23:02:04</td>\n",
              "      <td>2023-04-18 07:30:34</td>\n",
              "      <td>8.48</td>\n",
              "      <td>InBed</td>\n",
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              "      <td>2023-04-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-25 00:28:22</td>\n",
              "      <td>2023-04-25 07:32:14</td>\n",
              "      <td>7.06</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-24</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 01:27:47</td>\n",
              "      <td>2023-05-02 07:32:16</td>\n",
              "      <td>6.07</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d9d95bc-1bff-4623-a539-ab5a8b6a736b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2d9d95bc-1bff-4623-a539-ab5a8b6a736b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2d9d95bc-1bff-4623-a539-ab5a8b6a736b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Tue.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZ5wpeaA4iXP",
        "outputId": "07e7d1b6-e8b6-4718-cb1a-20d0f17e015b"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10 entries, 0 to 0\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Start           10 non-null     object \n",
            " 1   End             10 non-null     object \n",
            " 2   Duration (hr)   10 non-null     float64\n",
            " 3   Value           10 non-null     object \n",
            " 4   Source          10 non-null     object \n",
            " 5   year-month-day  10 non-null     object \n",
            " 6   weekday         10 non-null     int64  \n",
            "dtypes: float64(1), int64(1), object(5)\n",
            "memory usage: 640.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#월~금 합쳐서 경향보기 \n",
        "weekday=pd.concat([Mon,Tue,Wed,Thur,Fri],axis=0)"
      ],
      "metadata": {
        "id": "JdjGmaWk49vM"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weekday"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RMjt5x8d7Kiu",
        "outputId": "f7a1cdcd-5caa-4fce-e029-294522b8cf37"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr)  Value Source  \\\n",
              "0  2023-02-27 23:37:21  2023-02-28 07:00:35           7.39  InBed    NHC   \n",
              "0  2023-03-06 22:45:35  2023-03-07 07:30:14           8.74  InBed    NHC   \n",
              "0  2023-03-14 00:19:33  2023-03-14 07:30:35           7.18  InBed    NHC   \n",
              "0  2023-03-27 22:38:27  2023-03-28 07:30:14           8.86  InBed    NHC   \n",
              "0  2023-04-03 23:28:26  2023-04-04 07:30:21           8.03  InBed    NHC   \n",
              "0  2023-04-11 00:18:41  2023-04-11 07:30:16           7.19  InBed    NHC   \n",
              "0  2023-04-17 23:02:04  2023-04-18 07:30:34           8.48  InBed    NHC   \n",
              "0  2023-04-25 00:28:22  2023-04-25 07:32:14           7.06  InBed    NHC   \n",
              "0  2023-05-02 01:27:47  2023-05-02 07:32:16           6.07  InBed    NHC   \n",
              "0  2023-02-21 23:53:36  2023-02-22 07:45:24           7.86  InBed    NHC   \n",
              "0  2023-03-07 22:49:19  2023-03-08 07:07:01           8.30  InBed    NHC   \n",
              "0  2023-03-15 00:04:29  2023-03-15 07:30:24           7.43  InBed    NHC   \n",
              "0  2023-03-28 22:30:00  2023-03-29 07:30:22           9.01  InBed    NHC   \n",
              "0  2023-04-04 23:07:47  2023-04-05 07:30:13           8.37  InBed    NHC   \n",
              "0  2023-04-11 22:47:31  2023-04-12 07:30:15           8.71  InBed    NHC   \n",
              "0  2023-04-18 22:32:08  2023-04-19 07:30:52           8.98  InBed    NHC   \n",
              "0  2023-04-25 23:48:54  2023-04-26 07:40:03           7.85  InBed    NHC   \n",
              "0  2023-05-02 23:52:35  2023-05-03 07:35:09           7.71  InBed    NHC   \n",
              "0  2023-03-21 22:30:00  2023-03-22 07:27:13           8.95  InBed    NHC   \n",
              "0  2023-02-23 00:07:13  2023-02-23 07:30:26           7.39  InBed    NHC   \n",
              "0  2023-03-08 22:50:41  2023-03-09 07:29:37           8.65  InBed    NHC   \n",
              "0  2023-03-15 23:31:45  2023-03-16 07:40:04           8.14  InBed    NHC   \n",
              "0  2023-03-30 00:25:36  2023-03-30 07:30:13           7.08  InBed    NHC   \n",
              "0  2023-04-05 22:39:45  2023-04-06 07:30:10           8.84  InBed    NHC   \n",
              "0  2023-04-13 00:09:05  2023-04-13 07:30:14           7.35  InBed    NHC   \n",
              "0  2023-04-20 00:27:27  2023-04-20 07:30:18           7.05  InBed    NHC   \n",
              "0  2023-04-26 23:14:43  2023-04-27 07:35:11           8.34  InBed    NHC   \n",
              "0  2023-03-01 23:58:48  2023-03-02 07:18:20           7.33  InBed    NHC   \n",
              "0  2023-02-23 22:38:12  2023-02-24 07:30:25           8.87  InBed    NHC   \n",
              "0  2023-03-03 00:25:28  2023-03-03 07:30:13           7.08  InBed    NHC   \n",
              "0  2023-03-09 23:51:45  2023-03-10 07:30:19           7.64  InBed    NHC   \n",
              "0  2023-03-16 23:41:31  2023-03-17 07:30:16           7.81  InBed    NHC   \n",
              "0  2023-03-23 22:30:00  2023-03-24 07:36:23           9.11  InBed    NHC   \n",
              "0  2023-03-30 22:39:19  2023-03-31 07:20:11           8.68  InBed    NHC   \n",
              "0  2023-04-06 23:40:17  2023-04-07 07:30:11           7.83  InBed    NHC   \n",
              "0  2023-04-14 00:13:31  2023-04-14 07:30:03           7.28  InBed    NHC   \n",
              "0  2023-04-21 00:32:24  2023-04-21 07:30:14           6.96  InBed    NHC   \n",
              "0  2023-04-28 01:11:15  2023-04-28 07:30:19           6.32  InBed    NHC   \n",
              "0  2023-02-25 01:40:05  2023-02-25 07:30:00           5.83  InBed    NHC   \n",
              "0  2023-03-04 00:01:28  2023-03-04 07:30:00           7.48  InBed    NHC   \n",
              "0  2023-03-18 00:29:57  2023-03-18 07:30:00           7.00  InBed    NHC   \n",
              "0  2023-03-24 23:42:54  2023-03-25 07:30:00           7.79  InBed    NHC   \n",
              "0  2023-04-01 00:03:12  2023-04-01 07:30:00           7.45  InBed    NHC   \n",
              "0  2023-04-08 00:27:34  2023-04-08 07:30:00           7.04  InBed    NHC   \n",
              "0  2023-04-14 23:24:33  2023-04-15 07:30:01           8.09  InBed    NHC   \n",
              "0  2023-04-22 00:40:57  2023-04-22 07:30:10           6.82  InBed    NHC   \n",
              "0  2023-04-29 01:02:56  2023-04-29 07:30:00           6.45  InBed    NHC   \n",
              "\n",
              "  year-month-day  weekday  \n",
              "0     2023-02-27        0  \n",
              "0     2023-03-06        0  \n",
              "0     2023-03-13        0  \n",
              "0     2023-03-27        0  \n",
              "0     2023-04-03        0  \n",
              "0     2023-04-10        0  \n",
              "0     2023-04-17        0  \n",
              "0     2023-04-24        0  \n",
              "0     2023-05-01        0  \n",
              "0     2023-02-21        1  \n",
              "0     2023-03-07        1  \n",
              "0     2023-03-14        1  \n",
              "0     2023-03-28        1  \n",
              "0     2023-04-04        1  \n",
              "0     2023-04-11        1  \n",
              "0     2023-04-18        1  \n",
              "0     2023-04-25        1  \n",
              "0     2023-05-02        1  \n",
              "0     2023-03-21        1  \n",
              "0     2023-02-22        2  \n",
              "0     2023-03-08        2  \n",
              "0     2023-03-15        2  \n",
              "0     2023-03-29        2  \n",
              "0     2023-04-05        2  \n",
              "0     2023-04-12        2  \n",
              "0     2023-04-19        2  \n",
              "0     2023-04-26        2  \n",
              "0     2023-03-01        2  \n",
              "0     2023-02-23        3  \n",
              "0     2023-03-02        3  \n",
              "0     2023-03-09        3  \n",
              "0     2023-03-16        3  \n",
              "0     2023-03-23        3  \n",
              "0     2023-03-30        3  \n",
              "0     2023-04-06        3  \n",
              "0     2023-04-13        3  \n",
              "0     2023-04-20        3  \n",
              "0     2023-04-27        3  \n",
              "0     2023-02-24        4  \n",
              "0     2023-03-03        4  \n",
              "0     2023-03-17        4  \n",
              "0     2023-03-24        4  \n",
              "0     2023-03-31        4  \n",
              "0     2023-04-07        4  \n",
              "0     2023-04-14        4  \n",
              "0     2023-04-21        4  \n",
              "0     2023-04-28        4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff97a599-f65b-45c6-8cae-f029e862ced7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start</th>\n",
              "      <th>End</th>\n",
              "      <th>Duration (hr)</th>\n",
              "      <th>Value</th>\n",
              "      <th>Source</th>\n",
              "      <th>year-month-day</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-27 23:37:21</td>\n",
              "      <td>2023-02-28 07:00:35</td>\n",
              "      <td>7.39</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-06 22:45:35</td>\n",
              "      <td>2023-03-07 07:30:14</td>\n",
              "      <td>8.74</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-14 00:19:33</td>\n",
              "      <td>2023-03-14 07:30:35</td>\n",
              "      <td>7.18</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-27 22:38:27</td>\n",
              "      <td>2023-03-28 07:30:14</td>\n",
              "      <td>8.86</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-03 23:28:26</td>\n",
              "      <td>2023-04-04 07:30:21</td>\n",
              "      <td>8.03</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-03</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-11 00:18:41</td>\n",
              "      <td>2023-04-11 07:30:16</td>\n",
              "      <td>7.19</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-17 23:02:04</td>\n",
              "      <td>2023-04-18 07:30:34</td>\n",
              "      <td>8.48</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-25 00:28:22</td>\n",
              "      <td>2023-04-25 07:32:14</td>\n",
              "      <td>7.06</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 01:27:47</td>\n",
              "      <td>2023-05-02 07:32:16</td>\n",
              "      <td>6.07</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-21 23:53:36</td>\n",
              "      <td>2023-02-22 07:45:24</td>\n",
              "      <td>7.86</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-21</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-07 22:49:19</td>\n",
              "      <td>2023-03-08 07:07:01</td>\n",
              "      <td>8.30</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-15 00:04:29</td>\n",
              "      <td>2023-03-15 07:30:24</td>\n",
              "      <td>7.43</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-14</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-28 22:30:00</td>\n",
              "      <td>2023-03-29 07:30:22</td>\n",
              "      <td>9.01</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-04 23:07:47</td>\n",
              "      <td>2023-04-05 07:30:13</td>\n",
              "      <td>8.37</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-04</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-11 22:47:31</td>\n",
              "      <td>2023-04-12 07:30:15</td>\n",
              "      <td>8.71</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-18 22:32:08</td>\n",
              "      <td>2023-04-19 07:30:52</td>\n",
              "      <td>8.98</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-25 23:48:54</td>\n",
              "      <td>2023-04-26 07:40:03</td>\n",
              "      <td>7.85</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 23:52:35</td>\n",
              "      <td>2023-05-03 07:35:09</td>\n",
              "      <td>7.71</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-05-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-21 22:30:00</td>\n",
              "      <td>2023-03-22 07:27:13</td>\n",
              "      <td>8.95</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-21</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-23 00:07:13</td>\n",
              "      <td>2023-02-23 07:30:26</td>\n",
              "      <td>7.39</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-22</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-08 22:50:41</td>\n",
              "      <td>2023-03-09 07:29:37</td>\n",
              "      <td>8.65</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-15 23:31:45</td>\n",
              "      <td>2023-03-16 07:40:04</td>\n",
              "      <td>8.14</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-30 00:25:36</td>\n",
              "      <td>2023-03-30 07:30:13</td>\n",
              "      <td>7.08</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-05 22:39:45</td>\n",
              "      <td>2023-04-06 07:30:10</td>\n",
              "      <td>8.84</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-05</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-13 00:09:05</td>\n",
              "      <td>2023-04-13 07:30:14</td>\n",
              "      <td>7.35</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-20 00:27:27</td>\n",
              "      <td>2023-04-20 07:30:18</td>\n",
              "      <td>7.05</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-19</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-26 23:14:43</td>\n",
              "      <td>2023-04-27 07:35:11</td>\n",
              "      <td>8.34</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-26</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-01 23:58:48</td>\n",
              "      <td>2023-03-02 07:18:20</td>\n",
              "      <td>7.33</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-01</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-23 22:38:12</td>\n",
              "      <td>2023-02-24 07:30:25</td>\n",
              "      <td>8.87</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-03 00:25:28</td>\n",
              "      <td>2023-03-03 07:30:13</td>\n",
              "      <td>7.08</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-02</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-09 23:51:45</td>\n",
              "      <td>2023-03-10 07:30:19</td>\n",
              "      <td>7.64</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-16 23:41:31</td>\n",
              "      <td>2023-03-17 07:30:16</td>\n",
              "      <td>7.81</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-16</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-23 22:30:00</td>\n",
              "      <td>2023-03-24 07:36:23</td>\n",
              "      <td>9.11</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-30 22:39:19</td>\n",
              "      <td>2023-03-31 07:20:11</td>\n",
              "      <td>8.68</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-06 23:40:17</td>\n",
              "      <td>2023-04-07 07:30:11</td>\n",
              "      <td>7.83</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-06</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-14 00:13:31</td>\n",
              "      <td>2023-04-14 07:30:03</td>\n",
              "      <td>7.28</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-13</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-21 00:32:24</td>\n",
              "      <td>2023-04-21 07:30:14</td>\n",
              "      <td>6.96</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-20</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-28 01:11:15</td>\n",
              "      <td>2023-04-28 07:30:19</td>\n",
              "      <td>6.32</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-27</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-25 01:40:05</td>\n",
              "      <td>2023-02-25 07:30:00</td>\n",
              "      <td>5.83</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-24</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-04 00:01:28</td>\n",
              "      <td>2023-03-04 07:30:00</td>\n",
              "      <td>7.48</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-03</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-18 00:29:57</td>\n",
              "      <td>2023-03-18 07:30:00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-17</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-24 23:42:54</td>\n",
              "      <td>2023-03-25 07:30:00</td>\n",
              "      <td>7.79</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-24</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-01 00:03:12</td>\n",
              "      <td>2023-04-01 07:30:00</td>\n",
              "      <td>7.45</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-08 00:27:34</td>\n",
              "      <td>2023-04-08 07:30:00</td>\n",
              "      <td>7.04</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-07</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-14 23:24:33</td>\n",
              "      <td>2023-04-15 07:30:01</td>\n",
              "      <td>8.09</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-14</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-22 00:40:57</td>\n",
              "      <td>2023-04-22 07:30:10</td>\n",
              "      <td>6.82</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-21</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-29 01:02:56</td>\n",
              "      <td>2023-04-29 07:30:00</td>\n",
              "      <td>6.45</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-28</td>\n",
              "      <td>4</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-ff97a599-f65b-45c6-8cae-f029e862ced7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2=weekday['Start'] = pd.to_datetime(weekday['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')"
      ],
      "metadata": {
        "id": "J2yoApsA7RbL"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2=weekday"
      ],
      "metadata": {
        "id": "sMHoIkUY8Dsq"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yvjajrRR8vBy",
        "outputId": "2ba8928d-0478-4a21-c732-94b17ff9c91e"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                Start                  End  Duration (hr)  Value Source  \\\n",
              "0 2023-02-27 23:37:21  2023-02-28 07:00:35           7.39  InBed    NHC   \n",
              "0 2023-03-06 22:45:35  2023-03-07 07:30:14           8.74  InBed    NHC   \n",
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              "0 2023-04-03 23:28:26  2023-04-04 07:30:21           8.03  InBed    NHC   \n",
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              "0 2023-04-25 00:28:22  2023-04-25 07:32:14           7.06  InBed    NHC   \n",
              "0 2023-05-02 01:27:47  2023-05-02 07:32:16           6.07  InBed    NHC   \n",
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              "0 2023-03-03 00:25:28  2023-03-03 07:30:13           7.08  InBed    NHC   \n",
              "0 2023-03-09 23:51:45  2023-03-10 07:30:19           7.64  InBed    NHC   \n",
              "0 2023-03-16 23:41:31  2023-03-17 07:30:16           7.81  InBed    NHC   \n",
              "0 2023-03-23 22:30:00  2023-03-24 07:36:23           9.11  InBed    NHC   \n",
              "0 2023-03-30 22:39:19  2023-03-31 07:20:11           8.68  InBed    NHC   \n",
              "0 2023-04-06 23:40:17  2023-04-07 07:30:11           7.83  InBed    NHC   \n",
              "0 2023-04-14 00:13:31  2023-04-14 07:30:03           7.28  InBed    NHC   \n",
              "0 2023-04-21 00:32:24  2023-04-21 07:30:14           6.96  InBed    NHC   \n",
              "0 2023-04-28 01:11:15  2023-04-28 07:30:19           6.32  InBed    NHC   \n",
              "0 2023-02-25 01:40:05  2023-02-25 07:30:00           5.83  InBed    NHC   \n",
              "0 2023-03-04 00:01:28  2023-03-04 07:30:00           7.48  InBed    NHC   \n",
              "0 2023-03-18 00:29:57  2023-03-18 07:30:00           7.00  InBed    NHC   \n",
              "0 2023-03-24 23:42:54  2023-03-25 07:30:00           7.79  InBed    NHC   \n",
              "0 2023-04-01 00:03:12  2023-04-01 07:30:00           7.45  InBed    NHC   \n",
              "0 2023-04-08 00:27:34  2023-04-08 07:30:00           7.04  InBed    NHC   \n",
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              "0 2023-04-22 00:40:57  2023-04-22 07:30:10           6.82  InBed    NHC   \n",
              "0 2023-04-29 01:02:56  2023-04-29 07:30:00           6.45  InBed    NHC   \n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start</th>\n",
              "      <th>End</th>\n",
              "      <th>Duration (hr)</th>\n",
              "      <th>Value</th>\n",
              "      <th>Source</th>\n",
              "      <th>year-month-day</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-27 23:37:21</td>\n",
              "      <td>2023-02-28 07:00:35</td>\n",
              "      <td>7.39</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-06 22:45:35</td>\n",
              "      <td>2023-03-07 07:30:14</td>\n",
              "      <td>8.74</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-14 00:19:33</td>\n",
              "      <td>2023-03-14 07:30:35</td>\n",
              "      <td>7.18</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-27 22:38:27</td>\n",
              "      <td>2023-03-28 07:30:14</td>\n",
              "      <td>8.86</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-03 23:28:26</td>\n",
              "      <td>2023-04-04 07:30:21</td>\n",
              "      <td>8.03</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-03</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-11 00:18:41</td>\n",
              "      <td>2023-04-11 07:30:16</td>\n",
              "      <td>7.19</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-17 23:02:04</td>\n",
              "      <td>2023-04-18 07:30:34</td>\n",
              "      <td>8.48</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-25 00:28:22</td>\n",
              "      <td>2023-04-25 07:32:14</td>\n",
              "      <td>7.06</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 01:27:47</td>\n",
              "      <td>2023-05-02 07:32:16</td>\n",
              "      <td>6.07</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-21 23:53:36</td>\n",
              "      <td>2023-02-22 07:45:24</td>\n",
              "      <td>7.86</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-21</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-07 22:49:19</td>\n",
              "      <td>2023-03-08 07:07:01</td>\n",
              "      <td>8.30</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-15 00:04:29</td>\n",
              "      <td>2023-03-15 07:30:24</td>\n",
              "      <td>7.43</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-14</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-28 22:30:00</td>\n",
              "      <td>2023-03-29 07:30:22</td>\n",
              "      <td>9.01</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-04 23:07:47</td>\n",
              "      <td>2023-04-05 07:30:13</td>\n",
              "      <td>8.37</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-04</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-11 22:47:31</td>\n",
              "      <td>2023-04-12 07:30:15</td>\n",
              "      <td>8.71</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-18 22:32:08</td>\n",
              "      <td>2023-04-19 07:30:52</td>\n",
              "      <td>8.98</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-25 23:48:54</td>\n",
              "      <td>2023-04-26 07:40:03</td>\n",
              "      <td>7.85</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 23:52:35</td>\n",
              "      <td>2023-05-03 07:35:09</td>\n",
              "      <td>7.71</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-05-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-21 22:30:00</td>\n",
              "      <td>2023-03-22 07:27:13</td>\n",
              "      <td>8.95</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-21</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-23 00:07:13</td>\n",
              "      <td>2023-02-23 07:30:26</td>\n",
              "      <td>7.39</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-22</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-08 22:50:41</td>\n",
              "      <td>2023-03-09 07:29:37</td>\n",
              "      <td>8.65</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-15 23:31:45</td>\n",
              "      <td>2023-03-16 07:40:04</td>\n",
              "      <td>8.14</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-30 00:25:36</td>\n",
              "      <td>2023-03-30 07:30:13</td>\n",
              "      <td>7.08</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-29</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-05 22:39:45</td>\n",
              "      <td>2023-04-06 07:30:10</td>\n",
              "      <td>8.84</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-05</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-13 00:09:05</td>\n",
              "      <td>2023-04-13 07:30:14</td>\n",
              "      <td>7.35</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-20 00:27:27</td>\n",
              "      <td>2023-04-20 07:30:18</td>\n",
              "      <td>7.05</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-19</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-26 23:14:43</td>\n",
              "      <td>2023-04-27 07:35:11</td>\n",
              "      <td>8.34</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-26</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-01 23:58:48</td>\n",
              "      <td>2023-03-02 07:18:20</td>\n",
              "      <td>7.33</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-01</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-23 22:38:12</td>\n",
              "      <td>2023-02-24 07:30:25</td>\n",
              "      <td>8.87</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-03 00:25:28</td>\n",
              "      <td>2023-03-03 07:30:13</td>\n",
              "      <td>7.08</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-02</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-09 23:51:45</td>\n",
              "      <td>2023-03-10 07:30:19</td>\n",
              "      <td>7.64</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-16 23:41:31</td>\n",
              "      <td>2023-03-17 07:30:16</td>\n",
              "      <td>7.81</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-16</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-23 22:30:00</td>\n",
              "      <td>2023-03-24 07:36:23</td>\n",
              "      <td>9.11</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-30 22:39:19</td>\n",
              "      <td>2023-03-31 07:20:11</td>\n",
              "      <td>8.68</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-30</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-06 23:40:17</td>\n",
              "      <td>2023-04-07 07:30:11</td>\n",
              "      <td>7.83</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-06</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-14 00:13:31</td>\n",
              "      <td>2023-04-14 07:30:03</td>\n",
              "      <td>7.28</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-13</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-21 00:32:24</td>\n",
              "      <td>2023-04-21 07:30:14</td>\n",
              "      <td>6.96</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-20</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-28 01:11:15</td>\n",
              "      <td>2023-04-28 07:30:19</td>\n",
              "      <td>6.32</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-27</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-02-25 01:40:05</td>\n",
              "      <td>2023-02-25 07:30:00</td>\n",
              "      <td>5.83</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-02-24</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-04 00:01:28</td>\n",
              "      <td>2023-03-04 07:30:00</td>\n",
              "      <td>7.48</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-03</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-18 00:29:57</td>\n",
              "      <td>2023-03-18 07:30:00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-17</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-24 23:42:54</td>\n",
              "      <td>2023-03-25 07:30:00</td>\n",
              "      <td>7.79</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-24</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-01 00:03:12</td>\n",
              "      <td>2023-04-01 07:30:00</td>\n",
              "      <td>7.45</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-03-31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-08 00:27:34</td>\n",
              "      <td>2023-04-08 07:30:00</td>\n",
              "      <td>7.04</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-07</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-14 23:24:33</td>\n",
              "      <td>2023-04-15 07:30:01</td>\n",
              "      <td>8.09</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-14</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-22 00:40:57</td>\n",
              "      <td>2023-04-22 07:30:10</td>\n",
              "      <td>6.82</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-21</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-29 01:02:56</td>\n",
              "      <td>2023-04-29 07:30:00</td>\n",
              "      <td>6.45</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "      <td>2023-04-28</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d534d193-a545-49ce-ae46-0e441d04ac95')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d534d193-a545-49ce-ae46-0e441d04ac95 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d534d193-a545-49ce-ae46-0e441d04ac95');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2['Duration (hr)'].median() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoIm29ri8wOK",
        "outputId": "779ce3a2-8231-4e13-aa5a-4e936c8ec2b7"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.71"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2['Duration (hr)'].mean() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RY0xEkc186at",
        "outputId": "557da807-3882-4646-d3ee-01eadf26aee9"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.741914893617022"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2['Duration (hr)'].max() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulpu0TBL87r1",
        "outputId": "d0b2f522-e161-4322-a845-5e16b6bca474"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.11"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2['Duration (hr)'].min() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofM2Ln3A88R4",
        "outputId": "12122c04-4bfd-4a61-e753-b845cd51a5b7"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.83"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2['Duration (hr)'].mode() #최빈값"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QublzTPI8_LT",
        "outputId": "45400758-244f-41c0-b2cb-e7a03314f200"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7.08\n",
              "1    7.39\n",
              "Name: Duration (hr), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2[weekday2['Duration (hr)']==7.08].value_counts().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9XIjnPuB5Cp",
        "outputId": "0b78c076-52e1-4a0d-c296-462742a97e9d"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday2['Duration (hr)'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8WEgrDU_mXQ",
        "outputId": "22d7ddb3-c041-4d45-e760-88e159213b8b"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    47.000000\n",
              "mean      7.741915\n",
              "std       0.833838\n",
              "min       5.830000\n",
              "25%       7.130000\n",
              "50%       7.710000\n",
              "75%       8.425000\n",
              "max       9.110000\n",
              "Name: Duration (hr), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=weekday2['Duration (hr)'].value_counts()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdGWr2kt-MU_",
        "outputId": "3a30b75a-7948-411c-a6ab-abeb288e9f28"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.39    2\n",
              "7.08    2\n",
              "7.28    1\n",
              "8.34    1\n",
              "7.33    1\n",
              "8.87    1\n",
              "7.64    1\n",
              "7.81    1\n",
              "9.11    1\n",
              "8.68    1\n",
              "7.83    1\n",
              "6.96    1\n",
              "7.35    1\n",
              "6.32    1\n",
              "5.83    1\n",
              "7.48    1\n",
              "7.00    1\n",
              "7.79    1\n",
              "7.45    1\n",
              "7.04    1\n",
              "8.09    1\n",
              "6.82    1\n",
              "7.05    1\n",
              "8.84    1\n",
              "8.74    1\n",
              "8.30    1\n",
              "7.18    1\n",
              "8.86    1\n",
              "8.03    1\n",
              "7.19    1\n",
              "8.48    1\n",
              "7.06    1\n",
              "6.07    1\n",
              "7.86    1\n",
              "7.43    1\n",
              "8.14    1\n",
              "9.01    1\n",
              "8.37    1\n",
              "8.71    1\n",
              "8.98    1\n",
              "7.85    1\n",
              "7.71    1\n",
              "8.95    1\n",
              "8.65    1\n",
              "6.45    1\n",
              "Name: Duration (hr), dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 구간 설정\n",
        "bins = [0, 6, 7, 8, 9, 10]\n",
        "\n",
        "# 값들을 구간별로 나누기\n",
        "df['Duration (hr) bins'] = pd.cut(df.index, bins=bins)\n",
        "\n",
        "# 각 구간별 갯수 계산\n",
        "counts = df['Duration (hr) bins'].value_counts()\n",
        "\n",
        "print(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkezNTK7-xCn",
        "outputId": "86f13eaf-b2e2-4636-ed52-7d01b44ec708"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 6]      1\n",
            "(6, 7]      6\n",
            "(7, 8]     20\n",
            "(8, 9]     16\n",
            "(9, 10]     2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "이제 기준정했다.. "
      ],
      "metadata": {
        "id": "0p4Q7V5LBC2t"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}